Material processing optimization

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.62/786,655, filed on Dec. 31, 2018, the entire contents of which ishereby incorporated by reference.

BACKGROUND

This specification relates to optimizing processing of materials.

Industrial processing can include chemical, physical, electrical ormechanical steps to aid in the manufacturing of an item or items. Theoutputs of industrial processing vary based on conditions of theprocessing facility and the materials being processed.

SUMMARY

In general, one innovative aspect of the subject matter described inthis specification can be embodied in methods that include the actionsof collecting, from a set of sensors, a set of current manufacturingconditions; determining, based on the set of current manufacturingconditions collected from the sensors, a set of current qualities of amaterial currently being processed by manufacturing equipment; obtaininga baseline production measure for processing the material according tothe set of current qualities; determining a candidate set ofmanufacturing conditions that provide an improved production measurerelative to the baseline production measure; determining a set ofcandidate qualities for the material produced under the candidate set ofmanufacturing conditions; and generating a visualization that presentsboth of the set of candidate qualities of the material and the set ofcurrent qualities of the material currently being processed. Otherembodiments of this aspect include corresponding systems, apparatus, andcomputer programs, configured to perform the actions of the methods,encoded on computer storage devices.

These and other embodiments can each optionally include one or more ofthe following features. Determining a candidate set of manufacturingconditions that provide an improved production measure relative to thebaseline production measure can include determining a one or more of awood source mixture or power consumption that provide a candidateimproved production measure; and determining, based on a statisticalmodel of materials previously produced with various combinations ofmanufacturing conditions, a corresponding set of qualities of materialsprocessed with the one or more of the wood source mixture or the powerconsumption.

Determining a set of candidate qualities for the material produced underthe candidate set of manufacturing conditions can include comparing thecorresponding set of qualities to target qualities of completedmaterials; determining that the corresponding set of qualities meets thetarget qualities; and selecting the corresponding set of qualities asthe candidate set of qualities based on the determination that thecorresponding set of qualities meets the target qualities. Determiningthat the corresponding set of qualities meets the target qualities caninclude determining that one or more qualities among the correspondingset of qualities is within a custom quality value range specified by auser.

Generating a visualization that presents both of the set of candidatequalities of the material to the set of current qualities of thematerial currently being processed can include generating a first spidergraph that visually represents a first plurality of values of aplurality of different material qualities; generating a second spidergraph that visually represents a second plurality of values of theplurality of different material qualities; and incorporating both of thefirst spider graph and the second spider graph into a target qualitytemplate that depicts different zones including, for each targetquality, at least a meets target quality zone and a doesn't meet targetquality zone. The first spider graph can be color coded on a per-zonebasis.

Methods can include the operations of generating a visualization of thebaseline production measure, an actual production measure over a periodof time, and a computed production measure over that period of time,wherein the computed production measure provides the production measurefor production of materials under the candidate manufacturingconditions.

Methods can include the actions of changing a state of one or more paperpulp processing machines based on the candidate manufacturingconditions.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. The quality (e.g., characteristics) of a material(e.g., pulp) can be determined while the pulp is being produced, therebyenabling real time evaluation of the material quality duringmanufacturing. As such, the quality of the material can be changedwithout first having to produce the material, test the material, andthen produce another batch of material under new conditions. Thisreduces the time needed to arrive at materials having the desiredquality (e.g., characteristics), reduces manufacturing time, and reduceswaste. The systems and methods discussed herein can also outputrecommended (e.g., optimized) manufacturing conditions and/or change themanufacturing conditions to achieve materials of the desired quality.Simulations can be used for purposes of experimenting with differentchanges to the manufacturing conditions to obtain feedback as to qualityof the materials produced if the manufacturing conditions wereimplemented. The quality of materials can be evaluated/determined atdifferent points along the manufacturing process, rather than having towait to test a final manufactured materials. Desired qualities of themanufactured materials can be input and/or changed, and optimizedmanufacturing conditions for producing materials having those desiredqualities can be output or implemented by the system.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a user interface depicting a diagram of an example paper pulpprocessing system.

FIG. 2 is an example user interface in which a set of qualities arevisually presented.

FIG. 3 is an example of another user interface in which a set ofqualities are visually presented.

FIG. 4 is an example user interface in which the set of candidatequalities are graphically presented over a quality template.

FIG. 5 is an example user interface includes a history chart thatpresents changes to a production measure over time.

FIG. 6 is an example user interface that presents one or more qualitiesfor materials produced over a specified time period.

FIG. 7 is a block diagram of an example environment in whichoptimizations can be determined and/or implemented.

FIG. 8 is a flow chart of an example process of optimizing operation ofa manufacturing plant.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This document discusses techniques for optimizing the production ofmaterials during production. In other words, the techniques discussedherein enable real time adjustments to the manufacturing conditions tobe made, rather than having to wait until materials have been producedand tested, which can take hours or days. As such, the techniquesdiscussed in this document reduce the amount of time required to arriveat materials having a set of desired qualities, as well as reducing thewaste (e.g., material waste, energy waste, etc.) associated withtraditional “manufacture, test, and adjust” techniques that require thecompletion and testing of materials before manufacturing conditions canbe evaluated and/or modified. Thus, the present techniques improve thefunctioning of the equipment in the manufacturing plant by enabling theequipment to determine the quality of materials currently being producedand modify the manufacturing conditions so as to obtain more materialshaving more desirable qualities, also referred to as target qualities.For purposes of example, the techniques described in this document arediscussed with reference to pulp manufacturing, but the techniques canalso be applied to the processing of other materials.

As described in more detail throughout this document, the techniquesdescribed in this application generally collect information from sensorsdistributed throughout a manufacturing facility, and use thatinformation to determine a set of qualities of material currently beingprocessed by the manufacturing facility. For example, the sensors caninclude water quality sensors, flow sensors, temperature sensors,pressure sensors, and/or power sensors, among other sensors, which caneach report various manufacturing conditions to a server, or othercontrol system. As used throughout this document the phrase“manufacturing conditions” refers to one or more characteristics of amanufacturing/material processing environment, and can includeenvironmental conditions, information about materials being processed orbeing used as part of the processing, and/or equipment status, amongother conditions.

The set of current manufacturing conditions can be used to determine aset of current qualities of a material currently being processed by themanufacturing equipment. For example, in the context of paper pulpproduction, the set of current qualities can include one or more valuesfor opacity, bulk density (“Bulk”), tensile energy absorption (“TEA”),burst strength (“Burst”), tensile strength (“Tensile”), tear strength(“Tear”), Canadian Standard Freeness (“CSF”), amount of long-fiber(“Longfiber”), and/or other appropriate qualities. The set of currentqualities can be determined, for example, using a statistical model thattakes the current manufacturing conditions as input and outputs a set ofcurrent qualities that are expected for the materials currently beingproduced under the current manufacturing conditions.

In some situations, the production of the material can be optimized, forexample, to improve a production measure of the material processing. Asused throughout this document, the phrase “production measure” refers toa measure of production requirements. Some examples of productionrequirements include a total cost of production, an amount of materialsrequired for production, an amount of energy required for production, anamount of time required for production, or other requirements forproducing the materials. Changing the production measures can result inchanges to the qualities of the materials being produced. For example,in the context of paper pulp production, changing the amount of energyused to produce the pulp and/or changing the ratios of source woodmaterials will result in pulp having different characteristics, orqualities. However, the change to the qualities of the pulp (or othermaterial being produced) produced may still meet specified targetqualities, thereby providing an acceptable pulp, while improving theproduction measure.

As discussed in more detail below, user interface can be generated thatpresents a visualization (e.g., a graphical representation) of the setof current qualities for the material being produced and the differentqualities that the material will have if produced under the differentproduction measure(s). The visualization can include graphs (or othergraphical representations) that allow for direct comparison of the setof current qualities and the corresponding qualities that will resultfrom producing the material under the different production measure(s).

FIG. 1 is a user interface 100 depicting a diagram of an example paperpulp processing system. The system depicted in the user interface 100includes a pine source 102 that provides pine wood material forprocessing and a eucalyptus source 104 that provides eucalyptus woodmaterial for processing.

The system also includes refiner 106 (“Refiner A”) and refiner 108(“Refiner B”). Each of the refiners 106 and 108 can be, for example,disk refiners or other types of refiners. Disk refiners have rotatingdisks with serrated or other contoured surfaces, and one disk willrotate one direction, while the other disk rotates in an oppositedirection or remains fixed in place. The rotating disks cuts, frays, andsoftens the fibers of the wood material that is pumped into the refiners106 and 108. The space between the disks can be adjusted to change thedegree of refining performed by the refiners 106 and 108.

Generally speaking, the type of wood materials (or combinations of woodmaterials) and the amount of refining affect the qualities of thematerial output by the system 100. For example, if the fiber length isdecreased, the strength of the paper produced generally decreases, butthe smoothness generally increases. As the amount of refining isincreased (e.g., which corresponds to an increase in power consumption),the density, hardness, ink holdout, smoothness, and internal bondstrength will increase, but thickness, compressibility, and dimensionalstability will decrease.

In the system depicted by the user interface 100, the refiner 106processes a single wood material (e.g., pine material), while therefiner 108 processes a combination of wood material (e.g., pine andeucalyptus). As shown, a splitter 110 has an input connected to the pinesource 102, and has two outputs that direct pine wood material to eachof the refiner 106 and the refiner 108. In FIG. 1 , the splitter 110 isconfigured to direct 60% of the pine wood material received by thesplitter 110 to the refiner 106 by way of one of the outputs. Therefiner 106 then refines the pine wood material and outputs the refinedpine wood material. Meanwhile, the other output of the splitter 110directs the other 40% of the pine wood material towards the refiner 108.

A merger 112 is connected between the refiner 108 and each of thesplitter 110 and the eucalyptus source 104. More specifically, themerger 112 has two inputs, one of which is connected to the splitter110, and another input that is connected to the eucalyptus source 104.The merger 112 also has an output that is connected to the refiner 108.The merger 112 is configured to combine the pine wood material receivedfrom the splitter 110 with eucalyptus wood material that is receivedfrom the eucalyptus source 104, and pass the combination of woodmaterial to the refiner 108. In the present example, the merger 112 isconfigured to output a wood mixture that is 88% pine and 12% eucalyptus.The refiner 108 is configured to receive and process the wood mixtureprovided by the merger 112, and output the refined wood mixture.

The user interface 100 also includes a quality node 114 (“Quality NodeA”) that is located between the splitter 110 and the refiner 106 and aquality node 116 (“Quality Node B”) that is located at the output of therefiner 106. Each quality node is a software probe that provides aquality measure for the material being processed at that point in theprocessing pipeline. For example, the quality node 114 provides aquality measure of the material that is between the splitter 110 and therefiner 106, while the quality node 116 provides a quality measure ofthe material that is output from the refiner 106. The quality measuresprovided by the quality nodes can be computed based on a statisticalmodeling of historical quality data of materials at various points alongthe processing pipeline when manufacturing conditions were similar to,or the same as, the current manufacturing conditions.

In some situations, the graphical elements depicting the quality nodescan reveal quality information in response to user interaction with thegraphical elements. For example, a user click, tap, or other interactionwith either of the quality node 114 or the quality node 116 can causepresentation of one or more quality measures for the materials atdifferent points along the processing pipeline. As such, the qualitynodes enable real time evaluation and presentation of quality measuresfor materials being processed without having to stop processing thematerials, take a sample of the materials, or subject the samples tophysical/chemical testing. This real time evaluation of quality measureallows for real time changes to be made to the processing pipeline so asto adjust the qualities of the materials during processing, as discussedin more detail below.

As mentioned above, the quality of paper pulp can be evaluated in anumber of ways. For example, the quality of paper pulp can be evaluatedbased on a set of qualities that can include one or more of the opacity,bulk, TEA, burst, tensile, tear, CSF, and/or longfiber. In someimplementations, these qualities of the paper pulp can be presented to auser in a manner similar to the user interface depicted in FIG. 2 .

FIG. 2 is an example user interface 200 in which a set of qualities arevisually presented. The user interface 200 visually represents values ofthe qualities in different ways. For example, the user interface 200includes a first user interface pane (“pane”) 202 that includes a spidergraph 204 presented over a quality template 206. The quality template206 has an octagon shape made up of eight different triangle or pieportions. Each of the vertices of the octagon is assigned to acorresponding quality. For example, each of the opacity, bulk, TEA,burst, tensile, tear, CSF, and/or longfiber qualities is assigned to acorresponding vertex of the quality template 206.

The quality template 206 has multiple different zones that representdifferent quality levels. For example, the quality template 206 has anover-target zone 208 that corresponds to an over-target level of qualityfor each of the qualities. An over-target level of quality is a level ofquality that exceeds a target quality level (e.g., a discrete targetquality level or a target quality range). The quality template 206 alsohas an at-target zone 210 that corresponds to an at-target level ofquality for each of the qualities. An at-target level of quality is alevel of quality that meets the target quality level. The qualitytemplate also has an under-target zone 212 that corresponds to anunder-target level of quality for each of the qualities. An under-targetlevel of quality is a level of quality that does not meet the targetquality level.

A spider graph 214 (or another graph) can be generated and presentedover the quality template 206. The spider graph 214 visually representsvalues of the qualities that are assigned to the vertices of the qualitytemplate 206. In some implementations, the value of each quality isplaced along an axis that connects the center of the octagon with thevertex that is assigned to that quality. For example, the value ofopacity is represented by the dot 216, while the values of Longfiber andburst are represented by the dots 218 and 220, respectively. As shown,the value of opacity is located in the over-target zone 208, the valueof Longfiber is in the at-target zone 210, and the value of burst is inthe under-target zone 212.

The dots corresponding to the different quality measures of the materialbeing processed can be connected by lines, as shown. The area within thelines connecting the dots can then be color coded based on the portionof the quality template 206 that is overlaid by the area. For example,as shown in FIG. 2 , portions of the area within the lines that are overthe over-target zone 208 can be presented in a first color (orfill/shading), such as green or light grey. Meanwhile, the portions ofthe area within the lines that are over the at-target zone 210 can bepresented in a second color (or fill/shading), such as yellow or anothercolor, and the portions of the area within the lines that are over theunder-target zone 212 can be presented in a third color (orfill/shading), such as red or another color that visually differentiatesthis portion from the other portions.

Color coding the spider graph 214 (or other graph) in this mannerenables the user to quickly identify the overall quality of thematerials and to quickly identify any qualities that are not meeting apre-specified or target level. Furthermore, the quality summary depictedby way of the user interface 200 enables the computer to provide anintuitive summary of quality measures for multiple different qualitymetrics (or qualities) without requiring a user to launch separatequality metric interfaces or tables. As such, users are provided accessto a large amount of data in a more efficient manner (e.g., withouthaving to navigate or launch various different user interfaces orapplications).

The quality measure values can be normalized or otherwise adjusted sothat different value ranges can be uniformly (or consistently)represented/overlaid on the quality template 206. For example, thetarget value for each quality metric can be set at the dotted line 220that is within the at-target zone 210, and the value range for eachquality metric can be uniformly represented along a corresponding linebetween the center of the quality template to one of the vertices of thequality template 206. Other ways of normalizing or adjusting the qualitymeasures values to provide at a consistent graphical representation overthe quality template 206 can also be used.

The user interface 200 can include a toggle control 222 that causes theuser interface 200 to transition to the user interface 300 of FIG. 3 .The user interface 300 presents the quality information from the userinterface 200 in a tabular format, which enables the user to quickly seethe underlying values for each quality depicted in FIG. 2 . For example,the user interface 300 indicates that the opacity 302 has a value of46.23, and that the burst 304 of the material being processed is 50.50.The tabular information provided by the user interface 300 can also becolor coded in a manner similar to the color-coding used in the userinterface 200. For example, the value presented for the opacity 302 canbe color-coded with green (or some other fill/shading) indicating thatthe opacity value is within the over-target zone 208 of the qualitytemplate, while the value presented for the burst 304 can be color-codedwith red (or some other fill/shading) indicating that the burst value ofthe material is within the under-target zone 212 of the quality template206. The user interface 300 can also include a toggle control 306 thatcauses the user interface 200 to again be presented when a userinteracts with the toggle control 306.

As discussed in more detail below, the system that generates the userinterfaces 200 and 300 can include an optimizer that optimizes aproduction measure corresponding to the processing of the material. Asused throughout this document, the term “optimize” refers to animprovement in a given metric (or measure) over a current or referencemetric (or measure), and does not necessarily refer to a single optimalvalue. The production measure to be optimized can be any aspect ofmaterial processing that can be adjusted by making changes to themanufacturing conditions. In some situations, the production measure tobe optimized is total cost of manufacturing, while is other situations,the production measure to be optimized could be material waste, powerusage, or a specified quality of the material being processed.

The optimization of the production measure is achieved, for example, byadjusting various manufacturing conditions. For example, in paper pulpprocessing, the mixture of hardwood material and softwood material canbe adjusted, which can adjust the cost of producing the paper pulp. Inanother example, the power used during the processing of paper pulp canbe adjusted, for example, by changing refining settings and/or otherprocesses that utilize power during the processing. When manufacturingconditions are changed, the qualities of the material output by theprocessing will also change. For example, changing the wood mixturebeing processed will cause changes to various qualities such as burst,long-fiber, and other qualities.

The quality changes that will result from changing (or optimizing) oneor more production measures can be determined and/or quantified in amanner similar to the determination of the current qualities of thematerial currently being processed. For example, the system can utilizea statistical model of historical data to determine the qualities ofmaterials that would be generated under a proposed set of manufacturingconditions (e.g., candidate manufacturing conditions) that will providethe changed or optimized production measures. More specifically, acandidate set of manufacturing conditions that will achieve theoptimized production measures can be input into a statistical modelgenerated using historical data, including various differentcombinations of manufacturing conditions and corresponding qualities ofmaterials produced under those various different combinations ofmanufacturing conditions. The output of the statistical model will be aset of candidate qualities for the material that will be produced underthe candidate set of manufacturing conditions. The set of candidatequalities can be overlaid onto the quality template along with thecurrent qualities of the material currently being processed so as toallow for direct comparison of the two sets of qualities.

FIG. 4 is an example user interface 400 in which the set of candidatequalities are graphically presented over a quality template 402. The setof candidate qualities are represented as a spider graph 404, similar tothe spider graph 214. The user interface 400 also includes a spidergraph 406 that represents the current qualities of the materialscurrently being processed under the current manufacturing conditions.

Presenting the spider graph 404 and the spider graph 406 together overthe same quality template 402 enables direct comparison of the currentqualities of the materials currently being processed and the candidatequalities that will result if the manufacturing conditions are changedto achieve the optimized production measure(s). For example, the userinterface 400 shows that when the manufacturing conditions are changedto match the candidate set of manufacturing conditions, the long-fiberof the material being produced will fall, but that the burst of thatmaterial will increase.

This ability to visualize the changes to the qualities that will resultfrom changing the manufacturing conditions prevents the wasted time andresources (e.g., materials) that would result from trying differentcombinations of manufacturing conditions, testing the materials producedunder each combination of manufacturing materials, and further adjustingthe manufacturing conditions until arriving at a set of manufacturingconditions that not only provide an improved production measure relativeto a baseline production measure (e.g., current production measure), butalso provide materials that have a desired set of qualities. In someimplementations, the values of the candidate qualities can be includedin a tabular comparison 450 of the current qualities 452 and thecandidate qualities 454 that are presented in response to userinteraction with a toggle control 456 of the user interface 400.

The user interface 400 can include an optimize control 458 that caninitiate a number of actions. In some implementations, user interactionwith the optimize control 458 invokes the determination of the candidateset of manufacturing conditions and/or the set of candidate qualities ofmaterials that will be produced under the candidate set of manufacturingconditions. In some implementations, interaction with the optimizecontrol 458 (or another user interface element) can cause the system tochange a physical setting of at least one piece of manufacturingequipment to achieve the candidate set of manufacturing conditions. Forexample, the change in physical settings could be a change to a speed ofa refiner, a physical position of a valve (e.g., in a splitter, merger,or source), or another physical setting (or position) of another pieceof manufacturing equipment so as to change the manufacturing conditions.

Changes caused by changing manufacturing conditions can be trackedand/or displayed over time. FIG. 5 is an example user interface 500includes a history chart 502 that presents changes to a productionmeasure over time. The history chart 502 includes a line graph 504 thatrepresents the measured value of the production measure over time. Thehistory chart 502 also includes another line graph 506 that representsan optimized value of the production measure that would have resultedover time if the candidate set of manufacturing conditions had beenused. Presenting both of the line graph 504 and the line graph 506together in the history chart 502 enables direct comparison of themeasured value over time to the optimized value over time. Note thatother types of graphs could also be presented.

In some implementations, the line graph 504 can be color-coded based onthe value of the line graph relative to a baseline production measure(e.g., represented by the dotted line 508). The baseline productionmeasure 508 can be a pre-specified production measure, such as a targetproduction measure, an average production measure over time, or someother value of the production measure that can be used as a referencepoint for evaluating changes to the production measure over time. Thesystem can apply the color-coding, for example, by applying negativecolor (e.g., red) to portions of the line graph 504 representing valuesof the line graph 504 that are less desirable than the baselineproduction measure 508. Additionally, the area under the curve for theseless desirable portions of the graph 504 can also be color-coded usingthe negative color. The system can apply a positive color (e.g., green)to portions of the line graph 504 representing values of the line graph504 that are more desirable than the baseline production measure 508.Additionally, the area under the curve for these more desired portionsof the graph 504 can also be color-coded using the positive color.

In a specific example, the production measure can be a total cost ofmanufacturing, and the line graph 504 can represent the cost ofmanufacturing over a specified time period. Meanwhile, the line graph506 can represent the cost of manufacturing if the optimizations hasbeen implemented over that specified time period. In this example,portions of the line graph 504 having values that are higher than thebaseline cost 508 are the less desirable portions of the line graph 504,while portions of the graph having values that are lower than thebaseline cost 508 are the more desirable portions of the line graph 504.As such, the portions of the line graph 504 can be color-coded accordingto the description above.

The user interface 500 also includes an aggregate production measurechart 510 that presents a bar graph 512 (or another type of graph)depicting an aggregate value of the production measure over thespecified time period. In some implementations, the bar graph 512 has avalue corresponding to the difference between the aggregate baselineproduction measure over the specified period relative to the aggregatevalue of the measured production measure over the specified time period.For example, the bar graph 512 shows that the total cost ofmanufacturing over the specified period was $35 higher than the baselinecost over that specified time period.

The aggregate production measure chart 510 also presents another bargraph 514 depicting an aggregate value of the production measure thatwould have been realized over the specified time period had theoptimizations been implemented. In some implementations, the bar graph514 has a value corresponding to the difference between the aggregatebaseline production measure over the specified period relative to theaggregate value of the production measure that would have been realizedover the specified time period if the optimizations had beenimplemented. For example, the bar graph 514 shows that the total cost ofmanufacturing over the specified period would have been $12 lower thanthe baseline cost over that specified time period if the optimizationshad been implemented.

FIG. 6 is an example user interface 600 that presents one or morequalities for materials produced over a specified time period. The userinterface 600 includes an aggregate quality zone 602 that presentsaggregate quality measures over the specified time period (e.g., in theform of a line graph). The aggregate quality measures can be generatedbased on a combination of the individual quality measures presented, forexample, in FIG. 2 . In some situations, the aggregate quality measureconveys an overall quality of the materials produced.

For example, a set of target qualities for the materials produced can beinput or set by the manufacturer (or client), and the aggregate qualitymeasure for those materials can be a value indicating how well thematerials actually produced meet the set of target qualities. In somesituations, the contribution of each quality measure to the aggregatequality measure can be weighted, for example, based on an importance ofthat quality to the manufacturer. Furthermore, the aggregate qualitymeasure can be normalized to a standardized scale (e.g., 0-4) andplotted in the aggregate quality zone 602.

The aggregate quality zone 602 can present a line graph 604 thatvisually represents the aggregate quality of the materials produced overtime. In some situations, the line graph 604 (and/or areas under theline/curve) can be color-coded according to the value of the aggregatequality over time in a manner similar to that described above withreference to the color-coding of production measure graphs. For example,lower levels of aggregate quality can have a different color (or shade)than higher levels of aggregate quality.

The aggregate quality zone 602 can also present a line graph 606 thatrepresents the aggregate quality of materials that would have beengenerated if the optimizations had been implemented over the specifiedtime period. Presenting the line graph 606 together with the line graph604 enables direct comparison of the actual quality over time with thequality that could have been realized using the optimizations. In otherwords, the collected quality data is used to create new data showing anaggregate quality, and new data showing the aggregate quality that wouldhave been realized under the optimizations are presented together forpurposes of evaluating the actual materials produced relative to whatwould have been produced if the manufacturing conditions had beenadjusted according to the optimizations.

The user interface 600 also includes a per-quality zone 608 thatpresents information about individual qualities over time. For example,the per-quality zone 608 presents the quality template 206 discussedwith reference to FIG. 2 , which is overlaid with a spider graph 610similar to the spider graph 214 discussed above with reference to FIG. 2. The quality template 206 is also overlaid with an optimized spidergraph 612 similar to the spider graph 404 of FIG. 4 . More specifically,the spider graph 610 shows quality measures of materials produced, whilethe spider graph 612 shows the quality measures of materials that wouldhave been produced if the optimizations (e.g., candidate set ofmanufacturing conditions) had been implemented. The spider graphs 610and 612 can be dynamically updated as a user moves a cursor 614 along atimeline of a line graph 616 to show the qualities of materials beingproduced at the time corresponding to the position of the cursor as wellas the qualities of materials that would have been produced at that timeif the optimizations had been implemented.

The line graph 616 represents the values of a particular quality(Longfiber in this example) of the materials produced over a specifiedtime period. The line graph 616 can be overlaid on a time-based qualitytemplate 618 that includes an under-target zone 620, an at-target zone622 and an over-target zone 624. These zones generally correspond to thesimilarly named zones of the quality template 206, but in a time-serieschart. Further, the line graph 616 can be color-coded based on thetime-based quality template 618 in a manner similar to that discussedwith respect to the quality template 206. For example, areas under theline graph 616 can be shaded (or colored) according to the portion ofthe quality template 618 that is under those areas of the line graph.

FIG. 7 is a block diagram of an example environment 700 in whichoptimizations can be determined and/or implemented. The environment 700includes a network 702, such as a local area network (LAN), a wide areanetwork (WAN), the Internet, or a combination thereof. The network 702connects a sensor network 704, a historical database 706, analysisapplications 708, an optimizer 710, statistical models 712, andmanufacturing equipment 714.

The sensor network 704 can include various sensors that are distributedthroughout a manufacturing plant. For example, the sensor network 704can include vibration sensors, valve monitors, temperature probes,pressure gauges, flow meters, and/or other sensors that can monitormanufacturing conditions of the manufacturing plant. The sensor networkstores the collected data in the historical database 706. The datastored in the historical database 706 can include, for example, sets ofmanufacturing conditions 716 and quality measures 718 of materialsproduced under the corresponding sets of manufacturing conditions. Forexample, for a given set of manufacturing conditions X, Y, Z, thehistorical database 706 can store quality measures Q₁₁-Q₁₈ of thematerials that were produced under those manufacturing conditions.

The manufacturing conditions and quality measures can be stored overtime, and used by one or more data processing apparatus to create thestatistical models 712. The statistical models 712 can be generated (andhosted) by model servers using the historical data 706. In somesituations, the statistical models can be generated using variousstatistical techniques and/or modeling techniques. For example, themodels can be generated using linear or logistic regression.

In some situations, the data used to generate the statistical models 712can include additional data generated by the analysis applications 708.For example, the analysis applications 708 can use the data collected bythe sensor network 704 to make determinations (or inferences) andgenerate new data that characterize the manufacturing conditions thatexist and/or the quality of the materials being produced. Thedeterminations made by the analysis applications 708 can similarly bestored in the historical data 706 and used in the generation of thestatistical models 712.

The optimizer 710 is one or more data processing apparatus (e.g.,hardware processors) that can optimize the operation of a manufacturingfacility using the statistical models 712, data from the sensor network704, and/or data from the analysis applications 708. In someimplementations, the optimizer 710 can be configured to carry out any orall of the operations and techniques discussed in this document. Forexample, the optimizer can determine current qualities of materialsbeing produced based on the manufacturing conditions that currentlyexist, identify a candidate set of manufacturing conditions that willprovide an improved production measure relative to a baseline productionmeasure, generate visualizations that represent qualities of materialscurrently being produced as well as qualities of materials that would beproduced using the candidate set of manufacturing conditions, andpresent the visualizations in user interfaces similar to those discussedabove.

Furthermore, the optimizer 710 can cause physical changes to theoperation of the manufacturing facility. For example, the optimizer 710can communicate with the manufacturing equipment 714 to change aphysical position of a valve, physically adjust a damper setting, changean amount of material flowing through a portion of the processingpipeline, or otherwise change the physical state of the plant equipment.

FIG. 8 is a flow chart of an example process 800 of optimizing operationof a manufacturing plant. Operations of the process 800 can beimplemented, for example, by the optimizer 710 and/or one or more dataprocessing apparatus. In some implementations, operation of the process800 can be implemented as instructions stored on a non-transitorycomputer readable medium, where execution of the instructions by one ormore data processing apparatus cause the one or more data processingapparatus to perform operations of the process 800.

A set of current manufacturing conditions are collected from a set ofsensors (802). The sensors can be a set of sensors distributedthroughout a manufacturing facility (e.g., a materials processingfacility, such as a paper pulp plant). For example, the sensors caninclude pressure sensors, temperature sensors, flow meters, vibrationsensors, or other sensors that collect data about the operation of amanufacturing facility. The set of manufacturing conditions can include,for example, characteristics of the source materials being processed(e.g., composition), flow rates, temperatures, equipment settings,environmental settings, equipment status (e.g., normal operation orabnormal operation states), or other characteristics of the environmentin which materials are being processed.

A set of current qualities of materials currently being processed bymanufacturing equipment are determined (804). The current qualities ofthe materials currently being processed can be determined, for example,by inputting the set of current manufacturing conditions into astatistical model that outputs the set of current qualities of materialsthat are generated under the current manufacturing conditions. Asdiscussed above, the statistical models can be generated based on ananalysis of qualities of materials processed under various differentcombinations of manufacturing conditions. Determining the currentqualities in this manner is performed without having to take themanufacturing equipment offline, and without having to await testresults, which is not possible if the actual materials being producedwere required to be physically tested.

A baseline production measure is obtained for processing the materialaccording to the set of current qualities (806). In someimplementations, the baseline production measure can be a pre-specifiedproduction measure, such as a target production measure, an averageproduction measure over time, or some other value of the productionmeasure that can be used as a reference point for evaluating changes tothe production measure over time. The baseline production measure can bespecified by a user of the system or determined based on historicaldata. The baseline production measure can be any aspect of materialprocessing that can be adjusted by making changes to the manufacturingconditions. In some situations, the baseline production measure can beany one of a total cost of manufacturing, waste, power usage, or aspecified quality of the material being processed.

A candidate set of manufacturing conditions that provide an improvedproduction measure relative to the baseline production measure isdetermined (808). In some implementations, the candidate set ofmanufacturing conditions includes one or more changes in physicalsettings of manufacturing equipment, such as a change to a speed of arefiner, a physical position of a valve (e.g., in a splitter, merger, orsource), a change to the composition of source material (e.g., changingratios of hardwood/softwood), or another change to another setting ofanother piece of manufacturing equipment so as to change themanufacturing conditions. The candidate set of manufacturing conditionscan be determined, for example, using historical plant data and/or astatistical model. For example, the current set of manufacturingconditions and the current production measure can be input into astatistical model that represents relationships between manufacturingconditions and the production measure. In this example, the output ofthe statistical model can be the set of manufacturing conditions thatwill improve the production measure.

In some implementations, the candidate set of manufacturing conditionscan be determined by determining a one or more of a wood source mixtureor power consumption that provide a candidate improved productionmeasure. More specifically, a statistical model generated based onmaterials previously produced with various combinations of manufacturingconditions can be used to determine those manufacturing conditions thatwill improve the performance measure while generating materials having acorresponding set of qualities. In other words, the determination of thecandidate set of manufacturing conditions can ensure that materialsprocessed with the one or more of the wood source mixture or the powerconsumption will provide the improved production measure, and ensurethat the materials produced will have a corresponding set of qualities(e.g., as specified by the user).

A set of candidate qualities for the material produced under thecandidate set of manufacturing conditions is determined (810). In someimplementations, the set of candidate qualities are the qualities of thematerial that will be produced when the candidate set of manufacturingconditions are used. For example, the set of candidate qualities caninclude the qualities discussed with reference to the spider graph 406of FIG. 4 .

In some implementations, the determination of the set of candidatequalities includes an evaluation of multiple different sets ofcorresponding qualities that would result from using various differentcombinations of manufacturing conditions. For example, assume that theuser has specified a set of target qualities (e.g., desired qualities)for materials output by the manufacturing facility. In this example,multiple different combinations of manufacturing conditions may resultin an improved production measure, but not all of these combinations ofmanufacturing conditions may generate materials having the set of targetqualities. As such, the process 800 can include a comparison of thecorresponding set of qualities for each combination of manufacturingconditions to target qualities of completed materials.

When it is determined that the corresponding set of qualities for aparticular combination of manufacturing conditions meets the targetqualities that corresponding set of qualities can be selected as thecandidate set of qualities and the corresponding combination ofmanufacturing conditions can be the candidate set of manufacturingconditions for the optimization. In some situations, the determinationthat the corresponding set of qualities meets the target qualitiesincludes a determination that one or more qualities among thecorresponding set of qualities is within a custom quality value rangespecified by a user. Furthermore, if more than one set of qualitiesmeets the target qualities, the corresponding set of qualities havingthe best aggregate quality measure can be selected.

A visualization that presents both of the set of candidate qualities ofthe material and the set of current qualities of the material currentlybeing processed is generated (812). In some implementations, thegeneration of the visualization can include generation of one or moreuser interfaces similar to those discussed above. In some situations,the visualization is generated to include a first spider graph thatrepresents multiple values of multiple different material qualities. Thevisualization can also be generated to include a second spider graphthat represents different values of the multiple different materialqualities. In these situations, both both of the first spider graph andthe second spider graph can be incorporated into a target qualitytemplate, as shown in FIGS. 3 and 4 . The target quality templatedepicts different zones including, for each target quality, at least ameets target quality zone and a doesn't meet target quality zone. Asdiscussed above with reference to FIG. 2 , the first spider graph can becolor coded on a per-zone basis.

A visualization of the baseline production measure, an actual productionmeasure over a period of time, and a computed production measure overthat period of time is generated (814). In some implementations, theactual production measure represents the production measure for actualmaterials being produced, while the computed production measure providesthe production measure for production of materials that would occurunder the candidate manufacturing conditions. This visualization can begenerated to include the features of the user interface 500 discussedabove with reference to FIG. 5 .

A state of one or more manufacturing conditions is changed based on thecandidate manufacturing conditions (816). In some implementations, thestate of one or more paper pulp processing machines is changed based onthe candidate manufacturing conditions. For example, as discussed above,a physical state of a valve, a refiner, or any other paper pulpprocessing equipment can be changed to match one of the manufacturingconditions specified by the candidate manufacturing conditions.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal, that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back-end, middleware, or front-end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a clientdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the client device). Data generated atthe client device (e.g., a result of the user interaction) can bereceived from the client device at the server.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. A method of optimizing material production,comprising: collecting, from a set of sensors, a set of currentmanufacturing conditions for manufacturing equipment; and at one or morepoints of a plurality of points along a processing pipeline for amaterial being processed by the manufacturing equipment and during theprocessing pipeline for the material, performing operations comprising:determining, during the processing of the material by the manufacturingequipment and using a model that takes the set of current manufacturingconditions as input, a set of current qualities of the material at thepoint along the processing pipeline; obtaining a baseline productionmeasure for processing the material at the point along the processingpipeline according to the determined set of current qualities of thematerial; determining a candidate set of manufacturing conditions forthe manufacturing equipment that provide an improved production measurerelative to the baseline production measure for the material at thepoint along the processing pipeline; determining, using the model, a setof candidate qualities for the material produced by the processingpipeline of the manufacturing equipment corresponding to the candidateset of manufacturing conditions; in response to determining that the setof candidate qualities meets target qualities for materials produced bythe processing pipeline, applying a physical adjustment to themanufacturing equipment at the point along the processing pipeline basedon the candidate set of manufacturing conditions; and generating andpresenting a visualization that presents both of the set of candidatequalities of the material and the set of current qualities of thematerial currently being processed.
 2. The method of claim 1, whereinthe model is a statistical model and wherein determining a candidate setof manufacturing conditions for the manufacturing equipment that providean improved production measure for the material at the point of theprocessing pipeline relative to the baseline production measurecomprises: determining a one or more of a wood source mixture or powerconsumption that provide a candidate improved production measure; anddetermining, based on the statistical model, a corresponding set ofqualities of materials processed with the one or more of the wood sourcemixture or the power consumption.
 3. The method of claim 2, whereindetermining a set of candidate qualities for the material produced bythe processing pipeline of the manufacturing equipment under thecandidate set of manufacturing conditions comprises: comparing thecorresponding set of qualities to the target qualities of completedmaterials; determining that the corresponding set of qualities meets thetarget qualities; and selecting the corresponding set of qualities asthe candidate set of qualities based on the determination that thecorresponding set of qualities meets the target qualities.
 4. The methodof claim 3, wherein determining that the corresponding set of qualitiesmeets the target qualities comprises determining that one or morequalities among the corresponding set of qualities is within a customquality value range specified by a user.
 5. The method of claim 1,wherein generating a visualization that presents both of the set ofcandidate qualities of the material to the set of current qualities ofthe material currently being processed comprises: generating a firstspider graph that visually represents a first plurality of values of aplurality of different material qualities; generating a second spidergraph that visually represents a second plurality of values of theplurality of different material qualities; and incorporating both of thefirst spider graph and the second spider graph into a target qualitytemplate that depicts different zones including, for each targetquality, at least a meets target quality zone and a doesn't meet targetquality zone.
 6. The method of claim 5, wherein the first spider graphis color coded on a per-zone basis.
 7. The method of claim 1, furthercomprising generating a visualization of the baseline productionmeasure, an actual production measure over a period of time, and acomputed production measure over that period of time, wherein thecomputed production measure provides the production measure forproduction of materials under the candidate set of manufacturingconditions.
 8. The method of claim 1, further comprising changing astate of one or more paper pulp processing machines based on thecandidate set of manufacturing conditions.
 9. A system, comprising: adata storage device storing executable instructions; and one or moredata processing apparatus configured to interact with the data storagedevice and execute the instructions, wherein execution of theinstructions cause the one or more data processing apparatus to performoperations including: collecting, from a set of sensors, a set ofcurrent manufacturing conditions for manufacturing equipment; and at oneor more points of a plurality of points along a processing pipeline fora material being processed by the manufacturing equipment and during theprocessing pipeline for the material, performing operations comprising:determining, during the processing of the material by the manufacturingequipment and using a model that takes the set of current manufacturingconditions as input, a set of current qualities of the material at thepoint along the processing pipeline; obtaining a baseline productionmeasure for processing the material at the point along the processingpipeline according to the determined set of current qualities of thematerial; determining a candidate set of manufacturing conditions forthe manufacturing equipment that provide an improved production measurerelative to the baseline production measure for the material at thepoint along the processing pipeline; determining, using the model, a setof candidate qualities for the material produced by the processingpipeline of the manufacturing equipment corresponding to the candidateset of manufacturing conditions; in response to determining that the setof candidate qualities meets target qualities for materials produced bythe processing pipeline, applying a physical adjustment to themanufacturing equipment at the point along the processing pipeline basedon the candidate set of manufacturing conditions; and generating andpresenting a visualization that presents both of the set of candidatequalities of the material and the set of current qualities of thematerial currently being processed.
 10. The system of claim 9, whereinthe model is a statistical model and wherein determining a candidate setof manufacturing conditions for the manufacturing equipment that providean improved production measure for the material at the point of theprocessing pipeline relative to the baseline production measurecomprises: determining a one or more of a wood source mixture or powerconsumption that provide a candidate improved production measure; anddetermining, based on the statistical model, a corresponding set ofqualities of materials processed with the one or more of the wood sourcemixture or the power consumption.
 11. The system of claim 10, whereindetermining a set of candidate qualities for the material produced bythe processing pipeline of the manufacturing equipment under thecandidate set of manufacturing conditions comprises: comparing thecorresponding set of qualities to the target qualities of completedmaterials; determining that the corresponding set of qualities meets thetarget qualities; and selecting the corresponding set of qualities asthe candidate set of qualities based on the determination that thecorresponding set of qualities meets the target qualities.
 12. Thesystem of claim 11, wherein determining that the corresponding set ofqualities meets the target qualities comprises determining that one ormore qualities among the corresponding set of qualities is within acustom quality value range specified by a user.
 13. The system of claim9, wherein generating a visualization that presents both of the set ofcandidate qualities of the material to the set of current qualities ofthe material currently being processed comprises: generating a firstspider graph that visually represents a first plurality of values of aplurality of different material qualities; generating a second spidergraph that visually represents a second plurality of values of theplurality of different material qualities; and incorporating both of thefirst spider graph and the second spider graph into a target qualitytemplate that depicts different zones including, for each targetquality, at least a meets target quality zone and a doesn't meet targetquality zone.
 14. The system of claim 13, wherein the first spider graphis color coded on a per-zone basis.
 15. The system of claim 9, whereinexecution of the instructions causes the one or more data processingapparatus to perform operations including generating a visualization ofthe baseline production measure, an actual production measure over aperiod of time, and a computed production measure over that period oftime, wherein the computed production measure provides the productionmeasure for production of materials under the candidate set ofmanufacturing conditions.
 16. The system of claim 9, wherein executionof the instructions causes the one or more data processing apparatus toperform operations including changing a state of one or more paper pulpprocessing machines based on the candidate set of manufacturingconditions.
 17. A non-transitory computer storage medium encoded with acomputer program, the program comprising instructions that when executedby one or more data processing apparatus cause the one or more dataprocessing apparatus to perform operations comprising: collecting, froma set of sensors, a set of current manufacturing conditions formanufacturing equipment; and at one or more points of a plurality ofpoints along a processing pipeline for a material being processed by themanufacturing equipment and during the processing pipeline for thematerial, performing operations comprising: determining, during theprocessing of the material by the manufacturing equipment and using amodel that takes the set of current manufacturing conditions as input, aset of current qualities of the material at the point along theprocessing pipeline; obtaining a baseline production measure forprocessing the material at the point along the processing pipelineaccording to the determined set of current qualities of the material;determining a candidate set of manufacturing conditions for themanufacturing equipment that provide an improved production measurerelative to the baseline production measure for the material at thepoint along the processing pipeline; determining, using the model, a setof candidate qualities for the material produced by the processingpipeline of the manufacturing equipment corresponding to the candidateset of manufacturing conditions; in response to determining that the setof candidate qualities meets target qualities for materials produced bythe processing pipeline, applying a physical adjustment to themanufacturing equipment at the point along the processing pipeline basedon the candidate set of manufacturing conditions; and generating andpresenting a visualization that presents both of the set of candidatequalities of the material and the set of current qualities of thematerial currently being processed.
 18. The non-transitory computerstorage medium of claim 17, wherein the model is a statistical model andwherein determining a candidate set of manufacturing conditions for themanufacturing equipment that provide an improved production measure forthe material at the point of the processing pipeline relative to thebaseline production measure comprises: determining a one or more of awood source mixture or power consumption that provide a candidateimproved production measure; and determining, based on the statisticalmodel, a corresponding set of qualities of materials processed with theone or more of the wood source mixture or the power consumption.
 19. Thenon-transitory computer storage medium of claim 18, wherein determininga set of candidate qualities for the material produced by the processingpipeline of the manufacturing equipment under the candidate set ofmanufacturing conditions comprises: comparing the corresponding set ofqualities to the target qualities of completed materials; determiningthat the corresponding set of qualities meets the target qualities; andselecting the corresponding set of qualities as the candidate set ofqualities based on the determination that the corresponding set ofqualities meets the target qualities.
 20. The non-transitory computerstorage medium of claim 19, wherein determining that the correspondingset of qualities meets the target qualities comprises determining thatone or more qualities among the corresponding set of qualities is withina custom quality value range specified by a user.
 21. The non-transitorycomputer storage medium of claim 17, wherein generating a visualizationthat presents both of the set of candidate qualities of the material tothe set of current qualities of the material currently being processedcomprises: generating a first spider graph that visually represents afirst plurality of values of a plurality of different materialqualities; generating a second spider graph that visually represents asecond plurality of values of the plurality of different materialqualities; and incorporating both of the first spider graph and thesecond spider graph into a target quality template that depictsdifferent zones including, for each target quality, at least a meetstarget quality zone and a doesn't meet target quality zone.
 22. Thenon-transitory computer storage medium of claim 21, wherein the firstspider graph is color coded on a per-zone basis.
 23. The non-transitorycomputer storage medium of claim 17, wherein execution of theinstructions causes the one or more data processing apparatus to performoperations including generating a visualization of the baselineproduction measure, an actual production measure over a period of time,and a computed production measure over that period of time, wherein thecomputed production measure provides the production measure forproduction of materials under the candidate set of manufacturingconditions.
 24. The non-transitory computer storage medium of claim 17,wherein execution of the instructions causes the one or more dataprocessing apparatus to perform operations including changing a state ofone or more paper pulp processing machines based on the candidate set ofmanufacturing conditions.